The deployment of Artificial Intelligence (AI) in the financial services sector is bringing about advances in the quality of services offered, as well as efficiencies for financial service providers. However, the use of AI in finance can also give rise to new challenges and amplify existing risks. Thus, policy makers and regulators have an important role in ensuring that the use of AI in finance is consistent with promoting financial stability, protecting financial consumers, and promoting market integrity and competition.
Benefits of AI in Financial Services
AI stands to provide numerous benefits to the financial services sector, including:
- Improved accuracy and speed of automated processes and transactions.
- Reduced costs and improved customer experience.
- Increased accuracy in decision making.
- Improved customer segmentation and targeted marketing strategies.
- Automation of complex processes, such as portfolio management and risk management.
- Improved fraud detection and prevention.
Risks of AI in Financial Services
Despite the potential benefits of AI, the use of AI in finance can also give rise to new risks. These include:
- Data privacy and confidentiality issues.
- Unintended bias and discrimination of certain population groups.
- Data concentration and the potential impact on the competitive dynamics of the market.
- Lack of transparency and explainability of AI models.
- Pro-cyclicality, convergence, and increased market volatility.
- Systemic risks and vulnerabilities during periods of stress.
Policy Considerations for AI in Financial Services
In order to ensure that the use of AI in finance is consistent with promoting financial stability, protecting financial consumers, and promoting market integrity and competition, policy makers and regulators should consider the following:
- Strengthening data governance by financial sector firms, with a particular focus on data privacy, confidentiality, and potential biases.
- Introducing disclosure requirements around the use of AI techniques in the provision of financial services.
- Introducing suitability requirements for AI-driven financial services.
- Overcoming the incompatibility of the lack of explainability in AI with existing laws and regulations.
- Introducing clear model governance frameworks and attribution of accountability to the human.
- Providing increased assurance by financial firms around the robustness and resilience of AI models.
- Introducing frameworks for appropriate training, retraining, and rigorous testing of AI models.
- Introducing processes that allow customers to challenge the outcome of AI models and seek redress.
- Investing in research and skills for both finance sector participants and policy makers.
- Establishing a multidisciplinary dialogue at operational, regulatory, and supervisory level.
- Building bridges between disciplines to help improve explainability in AI.
- Enforcing authorities being technically capable of inspecting AI-based systems.
- Instilling trust and confidence in AI applications through clear communication.
The use of AI in financial services stands to bring numerous benefits to both financial service providers and consumers. However, the use of AI can also give rise to new risks, or amplify existing risks. Thus, it is important for policy makers and regulators to ensure that the use of AI in finance is consistent with promoting financial stability, protecting financial consumers, and promoting market integrity and competition.
Policy makers should consider strengthening data governance, introducing disclosure requirements, introducing suitability requirements, overcoming the incompatibility of the lack of explainability in AI with existing laws and regulations, introducing clear model governance frameworks, providing increased assurance by financial firms, introducing frameworks for appropriate training, retraining, and rigorous testing of AI models, introducing processes that allow customers to challenge the outcome of AI models and seek redress, investing in research and skills for both finance sector participants and policy makers, establishing a multidisciplinary dialogue, building bridges between disciplines to help improve explainability in AI, enforcing authorities being technically capable of inspecting AI-based systems, and instilling trust and confidence in AI applications through clear communication.